Overview

Dataset statistics

Number of variables12
Number of observations2988
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory291.8 KiB
Average record size in memory100.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qty_invoices and 3 other fieldsHigh correlation
qty_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qty_items_purchased is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
products_variety is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_variety_basketHigh correlation
recency_days is highly overall correlated with qty_invoicesHigh correlation
avg_recency_days is highly overall correlated with avg_freq_daysHigh correlation
avg_freq_days is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with gross_revenueHigh correlation
avg_variety_basket is highly overall correlated with qty_items_purchased and 2 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.59467161)Skewed
total_return is highly skewed (γ1 = 52.87135559)Skewed
avg_basket_size is highly skewed (γ1 = 44.74538965)Skewed
customer_id has unique valuesUnique
recency_days has 34 (1.1%) zerosZeros
total_return has 1500 (50.2%) zerosZeros

Reproduction

Analysis started2023-01-23 14:59:05.285487
Analysis finished2023-01-23 14:59:30.499279
Duration25.21 seconds
Software versionpandas-profiling vdev
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2988
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15269.023
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-01-23T11:59:30.632108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12613.7
Q113791.75
median15220.5
Q316770.25
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2978.5

Descriptive statistics

Standard deviation1721.2688
Coefficient of variation (CV)0.11272946
Kurtosis-1.2079407
Mean15269.023
Median Absolute Deviation (MAD)1490.5
Skewness0.029601368
Sum45623840
Variance2962766.2
MonotonicityNot monotonic
2023-01-23T11:59:30.842607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
16146 1
 
< 0.1%
17588 1
 
< 0.1%
14759 1
 
< 0.1%
16185 1
 
< 0.1%
13505 1
 
< 0.1%
13389 1
 
< 0.1%
17631 1
 
< 0.1%
17061 1
 
< 0.1%
17630 1
 
< 0.1%
Other values (2978) 2978
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12363 1
< 0.1%
12364 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2973
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2733.3693
Minimum5.9
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-23T11:59:30.999826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile227.5565
Q1566.595
median1079.475
Q32298.94
95-th percentile7169.562
Maximum279138.02
Range279132.12
Interquartile range (IQR)1732.345

Descriptive statistics

Standard deviation10528.004
Coefficient of variation (CV)3.851658
Kurtosis357.67231
Mean2733.3693
Median Absolute Deviation (MAD)667.97
Skewness16.852986
Sum8167307.5
Variance1.1083887 × 108
MonotonicityNot monotonic
2023-01-23T11:59:31.149073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
533.33 2
 
0.1%
178.96 2
 
0.1%
2053.02 2
 
0.1%
331 2
 
0.1%
889.93 2
 
0.1%
1314.45 2
 
0.1%
2092.32 2
 
0.1%
745.06 2
 
0.1%
598.2 2
 
0.1%
379.65 2
 
0.1%
Other values (2963) 2968
99.3%
ValueCountFrequency (%)
5.9 1
< 0.1%
6.2 1
< 0.1%
13.3 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
63 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
136275.72 1
< 0.1%
124564.53 1
< 0.1%
116729.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

qty_invoices
Real number (ℝ)

Distinct57
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6904284
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-23T11:59:31.339205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8214944
Coefficient of variation (CV)1.5502338
Kurtosis191.08365
Mean5.6904284
Median Absolute Deviation (MAD)2
Skewness10.766552
Sum17003
Variance77.818763
MonotonicityNot monotonic
2023-01-23T11:59:31.530552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 789
26.4%
3 498
16.7%
4 394
13.2%
5 236
 
7.9%
1 206
 
6.9%
6 173
 
5.8%
7 139
 
4.7%
8 98
 
3.3%
9 69
 
2.3%
10 54
 
1.8%
Other values (47) 332
11.1%
ValueCountFrequency (%)
1 206
 
6.9%
2 789
26.4%
3 498
16.7%
4 394
13.2%
5 236
 
7.9%
6 173
 
5.8%
7 139
 
4.7%
8 98
 
3.3%
9 69
 
2.3%
10 54
 
1.8%
ValueCountFrequency (%)
206 1
< 0.1%
198 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%

qty_items_purchased
Real number (ℝ)

Distinct467
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.08936
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-23T11:59:31.701901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median66
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation268.54062
Coefficient of variation (CV)2.1995416
Kurtosis356.32989
Mean122.08936
Median Absolute Deviation (MAD)44
Skewness15.718178
Sum364803
Variance72114.062
MonotonicityNot monotonic
2023-01-23T11:59:31.863175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 44
 
1.5%
20 37
 
1.2%
35 35
 
1.2%
15 34
 
1.1%
19 34
 
1.1%
29 34
 
1.1%
11 33
 
1.1%
25 32
 
1.1%
16 30
 
1.0%
27 30
 
1.0%
Other values (457) 2645
88.5%
ValueCountFrequency (%)
1 7
 
0.2%
2 14
0.5%
3 16
0.5%
4 17
0.6%
5 28
0.9%
6 28
0.9%
7 19
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5589 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2697 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1672 1
< 0.1%
1637 1
< 0.1%

products_variety
Real number (ℝ)

Distinct339
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.993641
Minimum1
Maximum1785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-23T11:59:32.277222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q125
median52
Q3100
95-th percentile233
Maximum1785
Range1784
Interquartile range (IQR)75

Descriptive statistics

Standard deviation96.628745
Coefficient of variation (CV)1.2232471
Kurtosis82.657166
Mean78.993641
Median Absolute Deviation (MAD)33
Skewness6.3984328
Sum236033
Variance9337.1145
MonotonicityNot monotonic
2023-01-23T11:59:32.470695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 44
 
1.5%
18 42
 
1.4%
37 40
 
1.3%
28 40
 
1.3%
25 37
 
1.2%
15 37
 
1.2%
11 37
 
1.2%
26 37
 
1.2%
23 36
 
1.2%
30 36
 
1.2%
Other values (329) 2602
87.1%
ValueCountFrequency (%)
1 26
0.9%
2 16
0.5%
3 21
0.7%
4 20
0.7%
5 35
1.2%
6 22
0.7%
7 26
0.9%
8 30
1.0%
9 32
1.1%
10 27
0.9%
ValueCountFrequency (%)
1785 1
< 0.1%
1766 1
< 0.1%
1322 1
< 0.1%
1118 1
< 0.1%
884 1
< 0.1%
816 1
< 0.1%
717 1
< 0.1%
713 1
< 0.1%
699 1
< 0.1%
636 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2986
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.975057
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-23T11:59:32.764030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9317949
Q113.113004
median17.936066
Q324.843993
95-th percentile90.498417
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.730989

Descriptive statistics

Standard deviation1033.7604
Coefficient of variation (CV)19.889548
Kurtosis2907.723
Mean51.975057
Median Absolute Deviation (MAD)5.9616373
Skewness53.594672
Sum155301.47
Variance1068660.6
MonotonicityNot monotonic
2023-01-23T11:59:32.931568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.162 2
 
0.1%
14.47833333 2
 
0.1%
25.31482759 1
 
< 0.1%
53.92173913 1
 
< 0.1%
15.94088235 1
 
< 0.1%
17.07774194 1
 
< 0.1%
17.22099526 1
 
< 0.1%
15.64627451 1
 
< 0.1%
13.54692308 1
 
< 0.1%
52.85080808 1
 
< 0.1%
Other values (2976) 2976
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
931.5 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.796519
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-23T11:59:33.119912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median32
Q384
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)73

Descriptive statistics

Standard deviation78.084432
Coefficient of variation (CV)1.2050714
Kurtosis2.7170346
Mean64.796519
Median Absolute Deviation (MAD)26
Skewness1.7841812
Sum193612
Variance6097.1785
MonotonicityNot monotonic
2023-01-23T11:59:33.304206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
3 85
 
2.8%
2 84
 
2.8%
8 76
 
2.5%
10 67
 
2.2%
7 66
 
2.2%
9 66
 
2.2%
17 64
 
2.1%
15 55
 
1.8%
Other values (262) 2239
74.9%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 84
2.8%
3 85
2.8%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.5%
9 66
2.2%
10 67
2.2%
ValueCountFrequency (%)
373 2
0.1%
372 3
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 3
0.1%
364 1
 
< 0.1%
360 2
0.1%
359 1
 
< 0.1%
358 4
0.1%

avg_recency_days
Real number (ℝ)

Distinct1254
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.898942
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-23T11:59:33.462093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.4458838
Q125.276786
median47.666667
Q385
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.723214

Descriptive statistics

Standard deviation63.463411
Coefficient of variation (CV)0.94864597
Kurtosis4.9415397
Mean66.898942
Median Absolute Deviation (MAD)26.143939
Skewness2.0734536
Sum199894.04
Variance4027.6046
MonotonicityNot monotonic
2023-01-23T11:59:33.632794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 24
 
0.8%
4 23
 
0.8%
70 22
 
0.7%
7 20
 
0.7%
1 19
 
0.6%
46 18
 
0.6%
21 18
 
0.6%
35 18
 
0.6%
11 18
 
0.6%
49 18
 
0.6%
Other values (1244) 2790
93.4%
ValueCountFrequency (%)
1 19
0.6%
1.5 1
 
< 0.1%
2 14
0.5%
2.5 1
 
< 0.1%
2.565517241 1
 
< 0.1%
3 15
0.5%
3.271929825 1
 
< 0.1%
3.321428571 1
 
< 0.1%
3.5 2
 
0.1%
4 23
0.8%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

avg_freq_days
Real number (ℝ)

Distinct1223
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.664859
Minimum0.058823529
Maximum183.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-23T11:59:33.812600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.058823529
5-th percentile1
Q119.986111
median38.472222
Q361
95-th percentile112.33333
Maximum183.5
Range183.44118
Interquartile range (IQR)41.013889

Descriptive statistics

Standard deviation34.172562
Coefficient of variation (CV)0.76508831
Kurtosis1.8971383
Mean44.664859
Median Absolute Deviation (MAD)20.472222
Skewness1.2356761
Sum133458.6
Variance1167.764
MonotonicityNot monotonic
2023-01-23T11:59:34.015710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 214
 
7.2%
16 18
 
0.6%
36 17
 
0.6%
42 16
 
0.5%
29 15
 
0.5%
11 15
 
0.5%
12 15
 
0.5%
34 14
 
0.5%
47 13
 
0.4%
39 13
 
0.4%
Other values (1213) 2638
88.3%
ValueCountFrequency (%)
0.05882352941 1
 
< 0.1%
0.3333333333 1
 
< 0.1%
0.5 7
 
0.2%
0.875 1
 
< 0.1%
1 214
7.2%
1.333333333 1
 
< 0.1%
1.5 3
 
0.1%
1.815533981 1
 
< 0.1%
1.883838384 1
 
< 0.1%
2 3
 
0.1%
ValueCountFrequency (%)
183.5 1
 
< 0.1%
183 1
 
< 0.1%
182.5 1
 
< 0.1%
182 1
 
< 0.1%
179 2
0.1%
178.5 1
 
< 0.1%
178 2
0.1%
176.5 1
 
< 0.1%
176 2
0.1%
175.5 3
0.1%

total_return
Real number (ℝ)

SKEWED  ZEROS 

Distinct173
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.872155
Minimum-0
Maximum80995
Zeros1500
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-23T11:59:34.238190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile0
Q1-0
median-0
Q36
95-th percentile61.65
Maximum80995
Range80995
Interquartile range (IQR)6

Descriptive statistics

Standard deviation1498.6991
Coefficient of variation (CV)28.892169
Kurtosis2851.7806
Mean51.872155
Median Absolute Deviation (MAD)0
Skewness52.871356
Sum154994
Variance2246099
MonotonicityNot monotonic
2023-01-23T11:59:34.419500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 1500
50.2%
1 295
 
9.9%
3 169
 
5.7%
6 93
 
3.1%
2 88
 
2.9%
4 71
 
2.4%
5 43
 
1.4%
12 43
 
1.4%
8 40
 
1.3%
7 38
 
1.3%
Other values (163) 608
20.3%
ValueCountFrequency (%)
-0 1500
50.2%
1 295
 
9.9%
2 88
 
2.9%
3 169
 
5.7%
4 71
 
2.4%
5 43
 
1.4%
6 93
 
3.1%
7 38
 
1.3%
8 40
 
1.3%
9 36
 
1.2%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
4824 1
< 0.1%
4027 1
< 0.1%
2302 2
0.1%
1776 1
< 0.1%
1608 1
< 0.1%
1589 1
< 0.1%
1515 1
< 0.1%
1278 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1986
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.2186
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-23T11:59:34.587759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.077778
Q1103.2875
median172.29167
Q3282.08333
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.79583

Descriptive statistics

Standard deviation789.44209
Coefficient of variation (CV)3.1550097
Kurtosis2265.1771
Mean250.2186
Median Absolute Deviation (MAD)83.25
Skewness44.74539
Sum747653.16
Variance623218.81
MonotonicityNot monotonic
2023-01-23T11:59:34.763423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 11
 
0.4%
82 9
 
0.3%
86 9
 
0.3%
136 9
 
0.3%
73 9
 
0.3%
60 9
 
0.3%
88 8
 
0.3%
75 8
 
0.3%
140 8
 
0.3%
Other values (1976) 2897
97.0%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

avg_variety_basket
Real number (ℝ)

Distinct911
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.536632
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.7 KiB
2023-01-23T11:59:34.961722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.6989247
median13.625
Q322.2
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.501075

Descriptive statistics

Standard deviation15.487218
Coefficient of variation (CV)0.88313525
Kurtosis28.890184
Mean17.536632
Median Absolute Deviation (MAD)6.625
Skewness3.4054
Sum52399.456
Variance239.85391
MonotonicityNot monotonic
2023-01-23T11:59:35.175719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 43
 
1.4%
9 42
 
1.4%
16 40
 
1.3%
8 39
 
1.3%
14 39
 
1.3%
17 38
 
1.3%
5 38
 
1.3%
7 38
 
1.3%
11 37
 
1.2%
15 35
 
1.2%
Other values (901) 2599
87.0%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
259 1
< 0.1%
177 1
< 0.1%
148 1
< 0.1%
127 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
95.5 1
< 0.1%
94.33333333 1
< 0.1%

Interactions

2023-01-23T11:59:27.876291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:05.984382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:07.935894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:09.952554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:11.797084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:13.856962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:16.006262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:17.979391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:19.851931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:21.827591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:23.750000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:25.892550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:28.000074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:06.160657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:08.093151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:10.108837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:11.927486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:13.988493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:16.190233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:18.181537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:20.022039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:22.021299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:23.914064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:26.045494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:28.165329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:06.296401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:08.270317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:10.316732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:12.085778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:14.185223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:16.382131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:18.305863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:20.167385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:22.196434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:24.043306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:26.188110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:28.318205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:06.451834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:08.430422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:10.441066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:12.209420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:14.310115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:16.485239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:18.428346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:20.302685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:22.306078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:24.191188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:26.345270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:28.472320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:06.563634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:08.574090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:10.578532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:12.367790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:14.479393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:16.675644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:18.586949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:20.483534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:22.450977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:24.589486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:26.517306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:28.618903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:06.688461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:08.753674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:10.733160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:12.623577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:14.656214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:16.823103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:18.766925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:20.616724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:22.618701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:24.738035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:26.700261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:28.815074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:06.857778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:08.967382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:10.864174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:12.756274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:14.812701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:16.959921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:18.887161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:20.786550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:22.760132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:24.879881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:26.819125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:28.957462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:07.020916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:09.179505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:10.982811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:12.905949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:14.958753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:17.175188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:19.086981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:20.951665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:22.960301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:25.074028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:26.957637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:29.129353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:07.259219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:09.345244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:11.132213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:13.103757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:15.081563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:17.293881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:19.268184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:21.108923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:23.136867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:25.244290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:27.141130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:29.257349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:07.468419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:09.493829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:11.319434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:13.309474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:15.515192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:17.422873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:19.399346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:21.302536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:23.280431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:25.384578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:27.343206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:29.486871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:07.605454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:09.668870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:11.487314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:13.525562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:15.681763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:17.585207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:19.592986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:21.489223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:23.435585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:25.537085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:27.550962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:29.733232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:07.785937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:09.805321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:11.679598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:13.675612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:15.871288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:17.762916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:19.741834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:21.632565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:23.635359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:25.733181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-23T11:59:27.695078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-23T11:59:35.322870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_idgross_revenueqty_invoicesqty_items_purchasedproducts_varietyavg_ticketrecency_daysavg_recency_daysavg_freq_daystotal_returnavg_basket_sizeavg_variety_basket
customer_id1.000-0.0760.0270.0110.006-0.1290.0010.0240.002-0.062-0.124-0.020
gross_revenue-0.0761.0000.7710.7450.6660.247-0.417-0.249-0.0790.3630.5730.104
qty_invoices0.0270.7711.0000.6910.5850.061-0.506-0.251-0.0600.2890.098-0.182
qty_items_purchased0.0110.7450.6911.0000.972-0.374-0.438-0.162-0.0250.2310.3810.513
products_variety0.0060.6660.5850.9721.000-0.453-0.383-0.1100.0110.1950.3840.636
avg_ticket-0.1290.2470.061-0.374-0.4531.0000.047-0.127-0.0870.1980.190-0.615
recency_days0.001-0.417-0.506-0.438-0.3830.0471.0000.106-0.028-0.121-0.0960.017
avg_recency_days0.024-0.249-0.251-0.162-0.110-0.1270.1061.0000.871-0.387-0.0860.128
avg_freq_days0.002-0.079-0.060-0.0250.011-0.087-0.0280.8711.000-0.223-0.0280.115
total_return-0.0620.3630.2890.2310.1950.198-0.121-0.387-0.2231.0000.208-0.064
avg_basket_size-0.1240.5730.0980.3810.3840.190-0.096-0.086-0.0280.2081.0000.403
avg_variety_basket-0.0200.104-0.1820.5130.636-0.6150.0170.1280.115-0.0640.4031.000

Missing values

2023-01-23T11:59:29.988482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-23T11:59:30.320573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenueqty_invoicesqty_items_purchasedproducts_varietyavg_ticketrecency_daysavg_recency_daysavg_freq_daystotal_returnavg_basket_sizeavg_variety_basket
0178505391.2134.0297.021.018.152222372.035.5000000.05882421.050.9705880.617647
1130473232.599.0171.0105.018.90403556.026.30769235.3333336.0154.44444411.666667
2125836705.3815.0232.0114.028.9025002.021.82352924.80000050.0335.2000007.600000
313748948.255.028.024.033.86607195.092.66666755.800000-0.087.8000004.800000
415100876.003.03.01.0292.000000333.08.60000013.66666722.026.6666670.333333
5152914623.3014.0102.061.045.32647125.021.75000024.92857127.0150.1428574.357143
6146885630.8721.0327.0148.017.2197867.018.30000017.476190281.0172.4285717.047619
7178095411.9112.061.046.088.71983616.032.45454529.83333341.0171.4166673.833333
81531160767.9091.02379.0567.025.5434640.04.1444444.109890231.0419.7142866.230769
9160982005.637.067.034.029.93477687.047.66666741.000000-0.087.5714294.857143
customer_idgross_revenueqty_invoicesqty_items_purchasedproducts_varietyavg_ticketrecency_daysavg_recency_daysavg_freq_daystotal_returnavg_basket_sizeavg_variety_basket
5612177271060.251.066.066.016.06439415.06.01.0000006.0645.00000066.000000
562217232421.522.036.030.011.7088892.012.06.500000-0.0101.50000015.000000
562317468137.002.05.05.027.40000010.04.02.500000-0.058.0000002.500000
563413596697.042.0166.0133.04.1990365.07.04.000000-0.0203.00000066.500000
5640148931237.852.073.072.016.9568499.02.01.500000-0.0399.50000036.000000
564412479473.201.030.030.015.77333311.04.01.00000034.0382.00000030.000000
566514126706.133.015.014.047.0753337.03.01.33333350.0169.3333334.666667
5671135211092.393.0435.0312.02.5112411.04.53.333333-0.0244.333333104.000000
568115060301.844.0120.080.02.5153338.01.00.500000-0.065.50000020.000000
570012558269.961.011.011.024.5418187.06.01.000000102.0196.00000011.000000